{
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import torch.nn as nn \n",
    "import torch.nn.functional as F \n",
    "import matplotlib.pyplot as plt \n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "torch.Size([100, 1])"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([-1.])"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "torch.Size([100, 1])"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "y = x*2+3\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([1.])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(x.data,y.data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self,input_dim,hidden_dim1,hidden_dim2,output_dim):\n",
    "        super(Net,self).__init__()\n",
    "        self.hidden1 = nn.Linear(input_dim,hidden_dim1)\n",
    "        self.hidden2 = nn.Linear(hidden_dim1,hidden_dim2)\n",
    "        self.get_y = nn.Linear(hidden_dim2,output_dim)\n",
    "\n",
    "    def forward(self,x):\n",
    "        out = self.hidden1(x)\n",
    "        out = F.relu(out)\n",
    "        out = self.hidden2(out)\n",
    "        out = F.relu(out)\n",
    "        out = self.get_y(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#输入是一个值，无视batch_size,默认x的输入都是算上batch_size的\n",
    "net = Net(1,64,32,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Net(\n  (hidden1): Linear(in_features=1, out_features=64, bias=True)\n  (hidden2): Linear(in_features=64, out_features=32, bias=True)\n  (get_y): Linear(in_features=32, out_features=1, bias=True)\n)\n"
     ]
    }
   ],
   "source": [
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#优化器与损失函数\n",
    "optimizer = torch.optim.SGD(net.parameters(),lr=0.001)\n",
    "loss_func = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "epoch:0 loss:0.003841218538582325\n",
      "epoch:100 loss:0.0035817718598991632\n",
      "epoch:200 loss:0.0033511044457554817\n",
      "epoch:300 loss:0.0031455536372959614\n",
      "epoch:400 loss:0.0029601941350847483\n",
      "epoch:500 loss:0.002793918363749981\n",
      "epoch:600 loss:0.0026448629796504974\n",
      "epoch:700 loss:0.0025090498384088278\n",
      "epoch:800 loss:0.0023876584600657225\n",
      "epoch:900 loss:0.0022761807776987553\n",
      "epoch:1000 loss:0.0021753960754722357\n",
      "epoch:1100 loss:0.0020819639321416616\n",
      "epoch:1200 loss:0.0019985316321253777\n",
      "epoch:1300 loss:0.0019205441931262612\n",
      "epoch:1400 loss:0.0018477129051461816\n",
      "epoch:1500 loss:0.0017818890046328306\n",
      "epoch:1600 loss:0.0017194891115650535\n",
      "epoch:1700 loss:0.0016610445454716682\n",
      "epoch:1800 loss:0.001606595003977418\n",
      "epoch:1900 loss:0.0015559899620711803\n",
      "epoch:2000 loss:0.0015078997239470482\n",
      "epoch:2100 loss:0.0014622423332184553\n",
      "epoch:2200 loss:0.0014183262828737497\n",
      "epoch:2300 loss:0.0013765086187049747\n",
      "epoch:2400 loss:0.0013365672202780843\n",
      "epoch:2500 loss:0.001298787072300911\n",
      "epoch:2600 loss:0.001262951991520822\n",
      "epoch:2700 loss:0.0012288910802453756\n",
      "epoch:2800 loss:0.0011961476411670446\n",
      "epoch:2900 loss:0.0011646137572824955\n",
      "epoch:3000 loss:0.0011342044454067945\n",
      "epoch:3100 loss:0.0011049170279875398\n",
      "epoch:3200 loss:0.0010767544154077768\n",
      "epoch:3300 loss:0.0010495855240151286\n",
      "epoch:3400 loss:0.0010236833477392793\n",
      "epoch:3500 loss:0.0009991227416321635\n",
      "epoch:3600 loss:0.0009761080727912486\n",
      "epoch:3700 loss:0.0009547944646328688\n",
      "epoch:3800 loss:0.0009351224289275706\n",
      "epoch:3900 loss:0.0009175362647511065\n",
      "epoch:4000 loss:0.0009012788068503141\n",
      "epoch:4100 loss:0.0008858597138896585\n",
      "epoch:4200 loss:0.0008708880050107837\n",
      "epoch:4300 loss:0.0008563037263229489\n",
      "epoch:4400 loss:0.00084217160474509\n",
      "epoch:4500 loss:0.0008284624782390893\n",
      "epoch:4600 loss:0.000815140490885824\n",
      "epoch:4700 loss:0.000802186201326549\n",
      "epoch:4800 loss:0.0007895163143984973\n",
      "epoch:4900 loss:0.0007772212265990674\n"
     ]
    }
   ],
   "source": [
    "#训练\n",
    "for i in range(5000):\n",
    "    predict = net(x)\n",
    "    loss = loss_func(predict,y)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if i%100 == 0 :\n",
    "        print('epoch:{}'.format(i),'loss:{}'.format(loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([5.])"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "#预测\n",
    "g_x = torch.Tensor([5])\n",
    "g_x\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([13.4302], grad_fn=<AddBackward0>)"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "prediction = net(g_x)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}